Stability Performance Of System Research Articles

A Biomimetic Pose Estimation and Target Perception Strategy for Transmission Line Maintenance UAVs

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption.

A blockchain consensus mechanism for real-time regulation of renewable energy power systems

With the ongoing development of renewable energy sources, information technologies and physical energy systems are further integrated, which leads to challenges in ensuring the secure and stable operation of renewable energy power systems in the face of potential cyber threats. The strengths of blockchain in cybersecurity make it a promising solution to these challenges. However, existing blockchains are not well-suited for control tasks due to their low real-time performance. Here, we present a consensus mechanism that enables real-time security control of systems, called Proof of Task. Instead of solving meaningless hash puzzles in Proof of Work, Proof of Task addresses problems closely related to the stable operation and control performance of these systems. With the proposed verification mechanism, Proof of Task significantly enhances the real-time performance of blockchain while mines its computational resources for tasks of interest. To demonstrate the effectiveness and necessity of Proof of Task, it is deployed across three renewable energy power systems. The results show that Proof of Task markedly fortifies the security and computing capability of these systems, ensuring their reliable and stable operation. This work highlights the promise of blockchain to facilitate security control and trusted computing of large-scale, complex-dynamic systems.

A multi-scale and multi-modal convolutional neural network for condition monitoring of transmission line

Abstract Monitoring the fatigue damage of transmission lines is crucial for stable power system operation. However, existing model-driven methods face challenges such as high computational complexity and reliance on expert knowledge, while data-driven methods require large amounts of abnormal state data. To address these issues, a multi-scale and multi-modal convolutional neural network (CNN) is proposed for real-time condition monitoring of transmission lines. Key steps include: firstly, empirical Fourier decomposition is used to decompose the original signals, extracting multi-scale state information at different frequency scales. Then, time-domain, frequency-domain, and time–frequency domain analyses are performed on the decomposed signals to capture multi-modal information. Based on this, a multi-modal fusion network is proposed based on a CNN to extract shallow and deep features, with a fully connected layer used for multi-modal feature fusion. Notably, the algorithm is implemented on a microprocessor for practical application. Experimental results show that the proposed model achieves a diagnostic accuracy of 93.06%, outperforming classical networks. It also surpasses models trained solely on time, frequency, or time–frequency features by 25.18%, 21.8%, and 19.3%, respectively.

Control Strategy for Power Fluctuation Smoothing at Distribution Network Substations Considering Multiple Types of Adjustment Resources

With the proposal of the dual carbon target, the distributed photovoltaic (PV) industry has rapidly developed in recent years. However, the randomness and volatility of photovoltaic energy can be transmitted to the main grid through distribution network substations, posing challenges to the stable operation of the power system. Therefore, this paper considers tapping into the regulation potential of feeder loads on the distribution network side, as well as distributed energy storage and distributed PV resources, to enhance the grid’s control methods. A power fluctuation smoothing control strategy for substations in distribution networks, accounting for multiple types of regulation resources, is proposed. In the day-ahead stage, traditional voltage regulation devices such as the OLTC (on-load tap changer) and CB (circuit breaker) are pre-dispatched based on source–load forecasts, optimizing the fluctuation range of substation power and the number of device operations. This provides optimal substation power values for day-to-day optimization. During the intraday phase, fast regulation devices such as PV (photovoltaic), SVC (static var compensator), and energy storage systems are coordinated, and an optimization model is established with the goal of reducing power curtailment while closely tracking substation trends. This model quickly calculates the active power regulation and device operations of various adjustable resources, improving the economic efficiency of the distribution network system while achieving power fluctuation smoothing at the substation level. Finally, the feasibility and effectiveness of the power fluctuation smoothing control model are verified through simulations on an improved standard distribution system.

Asymmetric LKF approach for stability analysis of multi-area load frequency control systems with time-varying delays

ABSTRACT The widespread use of communication networks in power systems to send and receive control signals causes time delays in the feedback loop. Improper handling of these delays might degrade controller performance or cause system instability. This work presents new stability criteria that are less conservative in analysing the influence of delays on the stable operation of PI controller-based multi-area power systems with uncertainties and disturbances. An asymmetric Lyapunov-Krasovskii functional technique is used to reduce conservatism by relaxing the condition that all matrices associated with the LKF formulation need not be symmetric or have positive definiteness. In contrast, the symmetric LKF requires all matrices to be symmetric. Two tight-bound integral inequalities are implemented to compute the quadratic integral terms included in the derivative of asymmetric LKF. The less conservative stability conditions are derived for multi-area LFC systems subjected to delays, parameter uncertainties, and disturbances by utilising asymmetric LKF. The superiority of the proposed stability criterion is determined both theoretically and through simulation. According to the analysis, the proposed stability criterion provides higher delay margins than existing techniques.

Neural network controller for hybrid energy management system applied to electric vehicles

Hybrid energy storage systems based on batteries and supercapacitors can mitigate the aging of electric vehicle batteries aging by avoiding high currents and rapid discharge cycles. This system requires energy management systems that efficiently split the power during real driving cycles. This paper outlines a design methodology for creating a Multilayer Perceptron neural controller that governs the power distribution between the storage system components. In parallel, a Proportional-Integral-Derivative controller was implemented to ensure voltage regulation in the primary DC bus, ensuring stable operation of the entire system and providing flexibility to the neural design. The controller was tuned using particle swarm optimization, hippopotamus optimization, and differential evolution algorithms, designed to minimize the battery root-mean-square current in a comprehensive vehicle simulation. The training was structured into layers to approach the physical problem and controller optimization facets. The controller’s primary goal is to minimize the battery strain, mitigating stress events and prolonging its lifespan. The energy management neural controller was designed using a simple drive cycle and later validated with a realistic cycle. The findings demonstrate the feasibility of achieving a significant reduction in current, both in peak value and on average, especially when compared to basic energy management strategies. The process uncovered a strategy that prioritizes the use of the supercapacitor in power balance, with this effect being more pronounced during critical load events. The main bus voltage remained stable, with no deviations exceeding 5%, owing to the voltage regulator’s stability. Additionally, the particle swarm optimization and the hippopotamus optimization techniques exhibited notable performance compared to the differential evolution algorithm for this problem. Subsequently, the design was evaluated in a more complex cycle, revealing a slight decrease in average battery current performance. However, it still maintained a significant reduction in peak battery current compared to traditional controllers. Nevertheless, this methodology demonstrated power management optimization efficacy and can be extended to more complex scenarios.

Load forecasting method based on CNN and extended LSTM

With the continuous development of power systems and the growing demand, accurate power load prediction has become essential to ensure stable system operation and optimize energy allocation. The collection and analysis of historical load data have become increasingly important, and the factors affecting load changes have grown more complex. To address this challenge, this paper proposes a short-term load prediction method based on convolutional neural networks (CNN) and multilayer extended long short-term memory (LSTM) neural networks. The method first extracts local features from historical data using 1D convolution operations in the CNN. Then, a multilayer extended LSTM network captures long-term dependencies in the data from multiple dimensions for more accurate load prediction. The prediction experiments use real load and related factor data from a city in eastern Mongolia. Results demonstrate that the proposed method outperforms other deep learning-based load prediction methods in direct single-step, four-step, and 32-step load predictions in terms of MAPE, RMSE, and MAE indexes. Additionally, the proposed model can be extended to more prediction application scenarios, including intelligent transportation and financial analysis.

Research on Testing Methods for the Modulus of Elasticity of Inorganic Binder-Strengthened Materials based on the Direct Strain Method

Abstract In order to obtain the elastic modulus of inorganic binder-stabilized materials based on the lateral method, firstly, by analyzing the constitutive model of the material’s elastic modulus, a testing system for the elastic modulus of inorganic binder-stabilized materials was constructed using pressure sensors, resistance strain gauges, and dynamic and static strain data testing systems. Secondly, based on the stability test of the strain gauges glued to the surface of the specimen, it is proposed that the zero drift of the strain gauges should be controlled within 0~6με within 6 minutes of the test load. Meanwhile, based on the experimental loading results, it is determined that the length of the strain gauge wire mesh used for modulus testing of inorganic binder-stabilised materials should be 30mm. Finally, elastic modulus testing of inorganic binder-stabilised materials was carried out on core samples drilled at Longxun highway in Guangdong Province. The modulus of elasticity of on-site drilled core specimens was determined by the test and the test results were at the same data level as those measured by the rigid ring method, indicating reliable analysis results. The experimental results show that the direct strain method with the elastic modulus testing system as the core can greatly reduce the test preparation time, ensure stable system operation and provide reliable test results.

Transformer fault diagnosis technology based on AdaBoost enhanced transferred convolutional neural network

Transformer fault diagnosis is an important means to ensure the stable operation of power systems, and Dissolved Gas Analysis (DGA) is a widely used method. However, existing intelligent diagnostic methods have problems such as insufficient expression of gas features, poor generalization of classification models, and lack of specialized deep optimization. On this basis, the authors proposed a transformer fault diagnosis technology based on AdaBoost enhanced transferred convolutional neural network, and constructed a transformer fault diagnosis scheme of “feature engineering, classification models, specialized deep optimization”. Firstly, the original five-dimensional gas features were expanded into new twenty-one-dimensional gas features to address the problem of insufficient expression of gas features. Factor analysis method was used to extract key information from twenty-one-dimensional gas features, and twelve-dimensional common factors were transformed to create the high-quality DGA data set. Secondly, the ensemble learning idea was referenced to construct ensemble classification models, and the best performing AdaBoost was obtained to solve the problem of poor generalization of single classification models. Finally, the transfer learning idea was referenced, the convolutional neural network was used as the basic classification model for AdaBoost, learning parameters were passed, and AdaBoost-TCNN with superior and stable classification effect was constructed to solve the problem of lack of specialized deep optimization. Test results on both private and public data sets show that, ensemble classification models achieved higher classification accuracy than single classification models, with significant advantages in Precision, Recall, and F1-score compared to single classification models. Among them, AdaBoost-TCNN achieved the highest classification accuracy of 97.33% and 95.35%, respectively, and obtained the most stable and significant Precision, Recall, and F1-score, all of which were superior to the ensemble classification models in the latest research. In addition, AdaBoost-TCNN also demonstrated a focus on key fault types. Therefore, the necessity of conducting this study, as well as the feasibility, practicality, and superiority of the technology proposed in this paper, have been fully demonstrated.

An Optimal Load Frequency Control in a Two-Area Power System Using a Fractional Order Proportional- Integral-Derivative-Based Zebra Optimization Algorithm

Load frequency control systems are crucial for maintaining the stability and reliability of power grids. They help ensure that the power supply matches the demand, preventing fluctuations in grid frequency. However, Load frequency control systems have inherent limitations, such as the potential for instability and oscillation if not properly controlled. In this work, A fractional order proportional integral derivative controller is proposed to address this issue, which exhibits strong capabilities in managing parameter uncertainties, rejecting disturbances, and handling non-linear systems controllers. The novel approach in this search is the application of the zebra optimization algorithm to fine-tune controller parameters, a technique not previously used in load frequency control systems. In this Study the two-area power system with a reheat turbine is used a test case for fractional order proportional integral derivative controller, and the system is simulated using MATLAB/SIMULINK. The objective function integral time of square error is used that acts as a bridge of communication between the system's behavior and the control strategy. The performance of this controller is evaluated under disturbance 0.04. The results of the analysis demonstrated that the fractional order proportional integral derivative based zebra optimization algorithm controller outperformed the traditional fractional order proportional integral derivative controller in terms of three essential criteria: overshoot, settling time, and steady-state error. This research contributes to the advancement of load frequency control systems, ensuring reliable and stable power system operation.

Power control of an autonomous wind energy conversion system based on a permanent magnet synchronous generator with integrated pumping storage

Wind energy plays a crucial role as a renewable source for electricity generation, especially in remote or isolated regions without access to the main power grid. The intermittent characteristics of wind energy make it essential to incorporate energy storage solutions to guarantee a consistent power supply. This study introduces the design, modeling, and control mechanisms of a self-sufficient wind energy conversion system (WECS) that utilizes a Permanent magnet synchronous generator (PMSG) in conjunction with a Water pumping storage station (WPS). The system employs Optimal torque control (OTC) to maximize power extraction from the wind turbine, achieving a peak power coefficient (Cp) of 0.43. A vector control strategy is applied to the PMSG, maintaining the DC bus voltage at a regulated 465 V for stable system operation. The integrated WPS operates in both motor and generator modes, depending on the excess or shortfall of generated wind energy relative to load demand. In generator mode, the WPS supplements power when wind speeds are insufficient, while in motor mode, it stores excess energy by pumping water to an upper reservoir. Simulation results, conducted in MATLAB/Simulink, show that the system efficiently tracks maximum power points and regulates key parameters. For instance, the PMSG successfully maintains the reference quadrature current, achieving optimal torque and power output. The system’s response under varying wind speeds, with an average wind speed of 8 m/s, demonstrates that the generator speed closely follows turbine speed without a gearbox, leading to efficient power conversion. The results confirm the flexibility and robustness of the control strategies, ensuring continuous power delivery to the load. This makes the system a feasible solution for isolated, off-grid applications, contributing to advancements in renewable energy technologies and autonomous power generation systems.

Multi-objective hierarchical energy management strategy for fuel cell/battery hybrid power ships

The energy management strategy and the local controller in the ship energy management system are interconnected, impacting the performance of the hybrid propulsion system. To achieve the efficient operation of the hydrogen fuel cell (FC) and battery hybrid power system, based on the modelling and analysis of the hybrid power system, a nonlinear model predictive control (NMPC) based energy management strategy is proposed, and a dynamic virtual impedance droop controller and a classical proportional-integral (PI) controller are designed as local controllers. By simulating the designed random load conditions, pulse load conditions, and actual sailing conditions using hardware-in-the-loop (HiLs) technology, six different energy management strategies and their comprehensive performance with local controllers are compared and analysed. Comparing performance in terms of energy consumption, operating pressure, control accuracy, real-time performance, and robustness, it has been proven that the energy management strategy based on NMPC, coupled with a PI controller, is superior to other strategies overall. It can balance hydrogen consumption and the stable operation of the hybrid power system. Compared to existing energy management strategies, the proposed NMPC+PI strategy can reduce hydrogen consumption by 7.00 % and 40.29 %, and FC operating pressure by 44.96 % and 49.88 %, respectively, under both designed navigation conditions and actual navigation conditions.

Learning about tail risk: Machine learning and combination with regularization in market risk management

High-quality risk management is the key to ensuring the safe, efficient, and stable operation of the financial system. The current Basel Accord requires financial institutions to regularly calculate and disclose Value at Risk (VaR) and Expected Shortfall (ES) measures. However, the inaccuracy and instability of traditional risk models have reduced users' confidence. Therefore, we propose two new probabilistic deep learning frameworks for estimating VaR and ES. The trained first framework can output expectiles that are more sensitive to tail risks to map VaR and ES measures. In the second framework, we propose to approximate VaR and ES measures with spline quantile function and estimate the parameters by designing various deep learning architectures. To ensure the effectiveness of the proposed architectures, we derived the training loss and constraints for them. In addition, we solve the problem that existing machine learning risk models are difficult to estimate ES. In this way, combining various individual risk models has great potential for risk management. Therefore, we propose a regularization-based combination framework that adaptively selects and shrinks individual risk models. The developed individual methods and combinations outperform existing methods in backtesting, assisting financial institutions to allocate capital more effectively according to the Basel Capital Accord.

A K-means cluster division of regional photovoltaic power stations considering the consistency of photovoltaic output

The uncertainty and volatility of photovoltaics seriously impact the grid's power quality. Short-term photovoltaic(PV) forecasts have a positive effect on the stable operation of the power system. The accuracy of cluster division is a key factor in the output prediction of regional PV power stations. This paper proposes a cluster division method, including a novel feature selection technique and an optimized cluster algorithm based on K-means. The proposed method performs feature analysis and parameter optimization of the division of regional photovoltaic plant clusters, analyzes the clustering dimension of photovoltaic output consistency, and establishes a K-means clustering model of photovoltaic power plants that considers time, space, and inherent characteristics of power plants first. Then, a prediction model based on Long Short-Term Memory (LSTM) is established for each cluster to realize the prediction of regional cluster photovoltaic output. The simulation results demonstrate that the Mean Absolute Percentage Error (MAPE) of the proposed method is 18.27 % and Root Mean Square Error (RMSE) is 45.79 %, which verifies the superiority of the proposed method over comparison models. It shows that the proposed method can effectively solve the problem of low prediction accuracy caused by weak output consistency of power stations in regional photovoltaic clusters.

Influence of Winding Configurations and Stator/Rotor Pole Combinations on Field Back-EMF Ripple in Switched Flux Hybrid Excited Machines

Similar to armature back electromotive force (armature back-EMF), the back-EMF also exists in the field winding of hybrid excited machines. However, the existence of field back electromotive force (field back-EMF) is harmful to the safe and stable operation of machine systems, e.g., higher losses, lower efficiency, higher torque ripple, and reduced control performance. This paper systematically investigates the influence of armature/field winding configurations together with stator/rotor pole combinations on the field back-EMF ripple in hybrid excited machines with switched-flux stators. The two-dimensional (2D) time-stepping finite element modeling and prototyping experiments are used for the research. The investigated field and armature coil pitches equal to 1, i.e., non-overlapped windings. The influential factors that are investigated in this paper mainly include the number of layers of field/armature windings, the number of field/armature coils, and the stator/rotor pole combinations. The results show that the field back-EMF’s harmonic order and peak-to-peak value are closely associated with field/armature winding configurations and stator/rotor pole combinations under various conditions. Finally, for validation of the results predicted by the finite element method, a prototype machine is built and tested. Overall, non-overlapped double-layer armature and field windings are recommended for the hybrid excited switched flux machines with various stator/rotor pole combinations to realize relatively lower field back-EMF under different conditions.

Non-singular fast terminal sliding mode trajectory tracking control of cantilever piezo-electric stack actuator based on asymmetric hysteresis compensation

Abstract Piezoelectric actuators are widely employed in micro-precision applications due to their fast response and high resolution. This paper investigates the trajectory tracking control of a cantilever piezoelectric stack actuator (CPSA) under external perturbations and hysteresis. A control scheme is proposed that effectively compensates for hysteresis by employing an asymmetric Bouc-Wen (ABW) model, integrated with a non-singular fast terminal sliding mode control (NFTSMC) featuring a variable convergence law. This methodology guarantees finite-time convergence and robustness, thereby enhancing the overall control performance of the CPSA. Experimental results reveal that the proposed control algorithm significantly improves control accuracy and speed, achieving stable closed-loop system performance and maintaining bounded closed-loop signals within a finite time frame. The effectiveness and superiority of the NFTSMC method are validated through comprehensive experimental studies.

A safety posture field framework for mobile manipulators based on human–robot interaction trend and platform-arm coupling motion

Mobile manipulators are increasingly deployed in industrial settings, such as material handling and workpiece loading, where they must safely interact with humans while efficiently completing tasks. Existing motion planning methods for mobile manipulators often struggle to ensure both safety and efficiency in dynamic human-robot interaction environments. This paper proposes a Safety Posture Field framework that addresses these limitations by firstly predicting human motion trends using the improved Long Short-Term Memory neural network and applying these predictions to potential field calculations for both the mobile platform and the robotic arm. During different stages of human-robot interaction, the mobile manipulator places varying emphasis on safety and efficiency while in motion. Additionally, when the robotic arm executes operations, a platform-arm coupling motion strategy is introduced when the potential field detects risks of singularity or local optima, preventing the robotic arm from becoming unstable or failing to reach the target pose in time. This strategy enhances the system's flexibility and operational stability. Comparative experiments in simulation and real-world settings confirm the ability of the framework to maintain high safety standards while improving task efficiency, making it suitable for industrial Human-Robot Interaction applications.

Strategies for enriching targeted sulfate-reducing bacteria and revealing their microbial interactions in anaerobic digestion ecosystems

Deciphering relationships between sulfate-reducing bacteria (SRB) and other microorganisms is crucial for stable operation of anaerobic digestion systems when treating sulfate-containing wastewater. However, few studies have differentiated the incomplete oxidizing SRB (IO-SRB) and complete oxidizing SRB (CO-SRB) in anaerobic digestion ecosystems. Four ethanol-fed bioreactors were operated under two operational modes (sequencing batch reactor, SBR; and continuous-flow reactor, CFR) and two chemical oxygen demand (COD) to sulfate ratios (1 and 2) to systematically explore strategies for enriching IO-SRB and/or CO-SRB and their microbial interactions with other microorganisms. Compared to SBRs, CFRs could enhance sulfate removal and demonstrated higher microbial activities in sulfate and ethanol degradation. IO-SRB competed with ethanol oxidizing bacteria in all reactors, and IO-SRB's contribution to ethanol degradation increased from 62.9 %-67.1 % to 69.0 %-82.1 % as the COD/sulfate ratio decreased from 2 to 1. Moreover, CO-SRB competed acetotrophic methanogens exclusively in CFRs, as CO-SRB could not be efficiently enriched in SBRs. Low COD/sulfate ratios facilitated the enrichment of Desulfococcus (CO-SRB), and the CFR operational mode further strengthened its enrichment. Additionally, hydrogenotrophic SRB outperformed hydrogenotrophic methanogens in all four reactors. In general, IO-SRB and CO-SRB possessed distinct microbial interactions with methanogens, with potential syntrophic relationships between IO-SRB and acetotrophic methanogens while competitive relationships between CO-SRB and acetotrophic methanogens.

Performance investigation of linear Fresnel concentrating photovoltaic systems with mini-channel heat sink using R141b/R245fa mixture enhanced by electric field

Reinforcement of heat transfer efficiency by electrohydrodynamics is a low-energy, high-efficiency technology. In this paper, we utilize the property of electrohydrodynamics to enhance heat transfer within a linear Fresnel concentrator photovoltaic (PV) system, with the anticipation of boosting the overall performance of the PV system in the process. The cooling fluid used in this work is R141b/R245fa mixture. In this paper, the output voltage, output current and inlet and outlet temperatures of the cooling fluid are tested in an outdoor environment. This paper analyzes the output power, system energy and environmental impact of a linear Fresnel PV system. The results show that the output power of the linear Fresnel PV system under the action of electric field (Applied voltage = 200 V) can reach a maximum of 11.45W and the surface average temperature of the solar cell can be maintained between 31 °C and 34 °C. When the applied voltage is 0 V, the average surface temperature of the solar cell fluctuates between 30 °C and 50 °C. Through the aforementioned study, it has been observed that the incorporation of the electric field effect can facilitate stable and efficient operation of the linear Fresnel photovoltaic system.

Robust Dynamic State Estimation for Power System Based on Generalized Correntropy Loss Function and Unscented Kalman Filter

Dynamic state estimation (DSE) of the power system is one of the most important means to ensure a safe and stable operation of the power system. However, the electromagnetic field environment, communication noise and equipment failure often lead to the appearance of non‐Gaussian noise and bad data, which ultimately cause performance degradation of traditional DSE algorithms based on the mean‐square error criterion. In this paper, a DSE method based on the generalized correntropy loss (GCL) function is proposed, which is well adapted to estimate the dynamic characteristics of the power system. The new robust DSE method can effectively deal with problems such as non‐Gaussian noise and bad data, thus improving the state estimation accuracy. Simulation results carried out on the IEEE 39‐bus system with synchronous generators demonstrate the robustness of the proposed DSE method. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.